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基于纳米复合拉伸传感器可穿戴阵列的开链康复运动中膝关节角度的精确预测。

Accurate Prediction of Knee Angles during Open-Chain Rehabilitation Exercises Using a Wearable Array of Nanocomposite Stretch Sensors.

机构信息

Department of Mechanical Engineering, Brigham Young University, Provo, UT 84602, USA.

Department of Exercise Science, Brigham Young University, Provo, UT 84602, USA.

出版信息

Sensors (Basel). 2022 Mar 24;22(7):2499. doi: 10.3390/s22072499.

Abstract

In this work, a knee sleeve is presented for application in physical therapy applications relating to knee rehabilitation. The device is instrumented with sixteen piezoresistive sensors to measure knee angles during exercise, and can support at-home rehabilitation methods. The development of the device is presented. Testing was performed on eighteen subjects, and knee angles were predicted using a machine learning regressor. Subject-specific and device-specific models are analyzed and presented. Subject-specific models average root mean square errors of 7.6 and 1.8 degrees for flexion/extension and internal/external rotation, respectively. Device-specific models average root mean square errors of 12.6 and 3.5 degrees for flexion/extension and internal/external rotation, respectively. The device presented in this work proved to be a repeatable, reusable, low-cost device that can adequately model the knee's flexion/extension and internal/external rotation angles for rehabilitation purposes.

摘要

本工作提出了一种用于膝关节康复物理治疗应用的膝关节护套。该设备配备了十六个压阻式传感器,可在运动过程中测量膝关节角度,并支持家庭康复方法。介绍了该设备的开发。对十八名受试者进行了测试,并使用机器学习回归器预测了膝关节角度。分析并呈现了受试者特定和设备特定的模型。受试者特定模型在屈伸和内外旋转方面的平均均方根误差分别为 7.6 和 1.8 度。设备特定模型在屈伸和内外旋转方面的平均均方根误差分别为 12.6 和 3.5 度。本工作中提出的设备被证明是一种可重复使用、成本低的设备,可充分模拟膝关节的屈伸和内外旋转角度,适用于康复目的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/442c/9003122/13946af23db8/sensors-22-02499-g001.jpg

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